AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service

Recent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of AI-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and AI-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described

Control Implementation in Bioprocess System: A Review.

Bioprocess control consists of establishing a strategy for the management of the biocatalyst environment. Bioprocesses include several different units in which a near optimal environment is desired for microorganisms to grow, multiply, and produce a desired product

Classifying intelligence in machines : a taxonomy of intelligent control

The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications. Looking at the spread of classifications based on this taxonomy can help researchers identify where control systems can be made more intelligent

Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance

This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity

Preparing, Characterizing, On-Line Digital Image Processing of Residence Time Distribution and Modeling of Mechanical Properties of Nanocomposite Foams

The objectives of this research were to prepare, characterize and to study the effects of organoclay and extrusion variables on the physical, mechanical, structural, thermal and functional properties of tapioca starch (TS)/poly(lactic acid) (PLA) nanocomposite foams. On-line digital imaging processing was used to determine residence time distribution (RTD). Adaptive neuro-fuzzy inference system (ANFIS) was used to model the mechanical properties of nanocomposite foams. Four different organoclays (Cloisite 10A, 25A, 93A, 15A) were used to produce nanocomposite foams by melt-intercalation. The properties were characterized using Xray diffraction, scanning electron microscopy, differential scanning calorimetric, and Instron universal testing machine. The properties were influenced significantly with the addition of different organoclays. TS/PLA/Cloisite 30B nanocomposite foams, with four clay contents of 1, 3, 5, 7 wt%, were prepared by a melt-intercalation method. Among the four nanocomposites, 3 wt% clay content produced significantly different properties. Screw speed, screw configuration, die nozzle diameter and moisture content were varied to determine their effects on organoclay intercalation. These extrusion variables had significant effects on the properties of TS/PLA /Cloisite 10A nanocomposite foams due to the intercalation of organoclay. Multiple inputs single output (MISO) models were developed to predict mechanical properties of nanocomposite foams. Four individual ANFIS models were developed. All models preformed well with R2 values \u3e 0.71 and had very low root mean squared errors (RMSE). Effects of screw configurations and barrel temperatures on the RTD and MISO models were developed to predict mechanical properties. The influence of the extrusion variables had a significant effect on the mean residence time (MTR). On-line digital image processing (DIP) technique was developed to measure the RTD as compared to the colorimeter method. R2 showed a correlation of 0.88 of a* values from both methods. The influence of screw configuration and temperature on RTD were analyzed by the MRT and variance for both methods. Mixing screws and lower temperature resulted in higher MRT and variance for both methods